Get exceptional consumer insights & market research using Facebook data

MicroStrategy Wisdom Professional unlocks an unprecedented new dimension for consumer research. This analytical application can explore the vast array of personal information contained in Facebook for millions of consumers including their demographics, interests, activities and preferences. Businesses and researchers use the extensive, accurate and up-to-date consumer data to improve a wide range of marketing activities including brand management, database marketing, media planning, competitive analysis, advertising, and social media marketing.

Attendees will:
• Learn the full spectrum of consumer information available for research in Wisdom Professional
• Find out the range of analytics and data enrichment offered in Wisdom Professional
• Understand how businesses can benefit from these consumer insights
• Watch demonstrations of specific business examples

This talk tells the story of implementation and optimization of a sparse logistic regression algorithm in spark. I would like to share the lessons I learned and the steps I had to take to improve the speed of execution and convergence of my initial naive implementation. The message isn’t to convince the audience that logistic regression is great and my implementation is awesome, rather it will give details about how it works under the hood, and general tips for implementing an iterative parallel machine learning algorithm in spark.

The talk is structured as a sequence of “lessons learned” that are shown in form of code examples building on the initial naive implementation. The performance impact of each “lesson” on execution time and speed of convergence is measured on benchmark datasets.

You will see how to formulate logistic regression in a parallel setting, how to avoid data shuffles, when to use a custom partitioner, how to use the ‘aggregate’ and ‘treeAggregate’ functions, how momentum can accelerate the convergence of gradient descent, and much more. I will assume basic understanding of machine learning and some prior knowledge of spark. The code examples are written in scala, and the code will be made available for each step in the walkthrough.

Lorand is a data scientist working on risk management and fraud prevention for the payment processing system of Zalando, the leading fashion platform in Europe. Previously, Lorand has developed highly scalable low-latency machine learning algorithms for real-time bidding in online advertising.

You can do a lot with a Raspberry and ASF projects. From a tiny object
connected to the internet to a small server application. The presentation
will explain and demo the following:

- Raspberry as small server and captive portal using httpd/tomcat.
- Raspberry as a IoT Sensor collecting data and sending it to ActiveMQ.
- Raspberry as a Modbus supervisor controlling an Industruino
(Industrial Arduino) and connected to ActiveMQ.

The 10x growth of transaction volumes, 50x growth in data volumes and drive for real-time visibility and responsiveness over the last decade have pushed traditional technologies including databases beyond their limits. Your choices are either buy expensive hardware to accelerate the wrong architecture, or do what other companies have started to do and invest in technologies being used for modern hybrid transactional analytical applications (HTAP).

Learn some of the current best practices in building HTAP applications, and the differences between two of the more common technologies companies use: Apache® Cassandra™ and Apache® Ignite™. This session will cover:

- The requirements for real-time, high volume HTAP applications
- Architectural best practices, including how in-memory computing fits in and has eliminated tradeoffs between consistency, speed and scale
- A detailed comparison of Apache Ignite and GridGain® for HTAP applications

About the speaker: Denis Magda is the Director of Product Management at GridGain Systems, and Vice President of the Apache Ignite PMC. He is an expert in distributed systems and platforms who actively contributes to Apache Ignite and helps companies and individuals deploy it for mission-critical applications. You can be sure to come across Denis at conferences, workshop and other events sharing his knowledge about use case, best practices, and implementation tips and tricks on how to build efficient applications with in-memory data grids, distributed databases and in-memory computing platforms including Apache Ignite and GridGain.

Before joining GridGain and becoming a part of Apache Ignite community, Denis worked for Oracle where he led the Java ME Embedded Porting Team -- helping bring Java to IoT.

Attend this session to learn how to easily share state in-memory across multiple Spark jobs, either within the same application or between different Spark applications using an implementation of the Spark RDD abstraction provided in Apache Ignite. During the talk, attendees will learn in detail how IgniteRDD – an implementation of native Spark RDD and DataFrame APIs – shares the state of the RDD across other Spark jobs, applications and workers. Examples will show how IgniteRDD, with its advanced in-memory indexing capabilities, allows execution of SQL queries many times faster than native Spark RDDs or Data Frames.

Akmal Chaudhri has over 25 years experience in IT and has previously held roles as a developer, consultant, product strategist and technical trainer. He has worked for several blue-chip companies such as Reuters and IBM, and also the Big Data startups Hortonworks (Hadoop) and DataStax (Cassandra NoSQL Database). He holds a BSc (1st Class Hons.) in Computing and Information Systems, MSc in Business Systems Analysis and Design and a PhD in Computer Science. He is a Member of the British Computer Society (MBCS) and a Chartered IT Professional (CITP).

When monitoring an increasing number of machines, the infrastructure and tools need to be rethinked. A new tool, ExDeMon, for detecting anomalies and raising actions, has been developed to perform well on this growing infrastructure. Considerations of the development and implementation will be shared.

Daniel has been working at CERN for more than 3 years as Big Data developer, he has been implementing different tools for monitoring the computing infrastructure in the organisation.

As data analytics becomes more embedded within organizations, as an enterprise business practice, the methods and principles of agile processes must also be employed.

Agile includes DataOps, which refers to the tight coupling of data science model-building and model deployment. Agile can also refer to the rapid integration of new data sets into your big data environment for "zero-day" discovery, insights, and actionable intelligence.

The Data Lake is an advantageous approach to implementing an agile data environment, primarily because of its focus on "schema-on-read", thereby skipping the laborious, time-consuming, and fragile process of database modeling, refactoring, and re-indexing every time a new data set is ingested.

With new technologies such as Hive LLAP or Spark SQL, do you still need a data warehouse or can you just put everything in a data lake and report off of that? No! In the presentation, James will discuss why you still need a relational data warehouse and how to use a data lake and an RDBMS data warehouse to get the best of both worlds.

James will go into detail on the characteristics of a data lake and its benefits and why you still need data governance tasks in a data lake. He'll also discuss using Hadoop as the data lake, data virtualization, and the need for OLAP in a big data solution, and he will put it all together by showing common big data architectures.

Watson is a computer system capable of answering questions posed in natural language. Watson was named after IBM's first CEO, Thomas J. Watson. The computer system was specifically developed to answer questions on the quiz show Jeopardy! (where it beat its human competitors) and was then used in commercial applications, the first of which was helping with lung cancer treatment.

NetApp is now using IBM Watson in Elio, a virtual support assistant that responds to queries in natural language. Elio is built using Watson’s cognitive computing capabilities. These enable Elio to analyze unstructured data by using natural language processing to understand grammar and context, understand complex questions, and evaluate all possible meanings to determine what is being asked. Elio then reasons and identifies the best answers to questions with help from experts who monitor the quality of answers and continue to train Elio on more subjects.

Elio and Watson represent an innovative and novel use of large quantities of unstructured data to help solve problems, on average, four times faster than traditional methods. Join us at this webcast, where we’ll discuss:

It can be hard to keep up with the rapidly changing BI landscape. But it doesn't have to be. Reserve your spot at Qlik's annual BI Trends Webinar.

In this global webinar live replay, we’ll reveal the top BI Trends for the coming year and how they can help you transform your data. Join Qlik’s Global Market Intelligence lead and former Gartner analyst Dan Sommer to learn why 2018 is the year for the “desilofication of data.”

Recent events like the Equifax data leak and new regulations like the EU's General Data Protection Regulation have increased the urgency for further change in the BI landscape and to move data out of silos.

What is the right strategy and framework?
How can you easily move from "all data," to "combinations of data," to "data insights"?
Can data literacy and augmented intelligence create a data-driven culture?
The volume of data available to decision makers continues to be massive, and is growing faster than our ability to consume it. Learn how to move your data out of silos and turn your data into insights.

RIDE supports developing in notebooks, editor, RMarkdown, shiny app, Bokeh and other frameworks. Supported by R-Brain’s optimized kernels, R and Python 3 have full language support, IntelliSense, debugger and data view. Autocomplete and content assistant are available for SQL and Python 2 kernels. Spark (standalone) and Tesnsorflow images are also provided.

Using Docker in managing workspaces, this platform provides an enhanced secure and stable development environment for users with a powerful admin control for controlling resources and level of access including memory usage, CPU usage, and Idle time.

The latest stable version of IDE is always available for all users without any need of upgrading or additional DevOps work. R-Brain also delivers customized development environment for organizations who are able to set up their own Docker registry to use their customized images.

The RIDE Platform is a turnkey solution that increases efficiency in your data science projects by enabling data science teams to work collaboratively without a need to switch between tools. Explore and visualize data, share analyses, all in one IDE with root access, connection to git repositories and databases.

IT is a key player in the digital and cognitive transformation of business processes delivering solutions for improved business value with analytics. This session will step by step explain the journey to secure production while adopting new analytics technologies leveraging mainframe core business assets

We will discuss how Big Data, Artificial Intelligence and Machine learning are rapidly impacting businesses and customers, enabling another massive shift through technology enablement. Todd DeCapua will share how these capabilities are being leveraged in Performance Engineering now, and into the future.

Join us for the next Quality & Testing SIG Talk on Tuesday, January 9, 2018: http://www.vivit-worldwide.org/events/EventDetails.aspx?id=1041157&group=.

Researchers generate huge amounts of valuable unstructured data and articles from research every day. The potential for this information is huge: cancer and pharmaceutical breakthroughs, advances in technology and cultural research that can improve the world we live in.

This webinar discusses how text mining and Machine Learning can be used to make connections across this broad range of files and help drive innovation and research. We discuss using Kubernetes microservices to analyse the data and then applying Machine Learning and graph databases to simplify the reuse of the data.

The data economy and digital technologies are deeply transforming almost all areas of our lives. One of the most heavily transformed revolve around insurance and healthcare with a number of really interesting development possibly redefining the way we take care of ourselves and the way we consumer and use insurance as well.

From harnessing the power of data to better help mental health patients, carers and medical personnel with their treatments to assessing the risk of developing broad range of illnesses and engaging better with users to propose them personalised healthy life plans to using big data and analytics to track down and prepare for epidemics to using data to better cover cars and drivers with car insurances and finally using social media data for insurers to better engage with customers, this webinar will propose a fascinating exploration of the opportunities, risks, new models supporting the digital transformation in banking.

Robert Fleming, Vice-President of International Marketing and Global Campaigns, Qlik

It's important to be aware of and respond to the key trends in Data Visualization now that we're heading into 2018.

Join this video panel, where you will learn:

-How to augment your intelligence across all your data, people and ideas
-Uncover and take advantage of new data sources
-Adjusting to the shift towards real-time enterprise
-Deployment of new workloads like IoT towards the cloud
-Why on-remise is still the predominant deployment model
-Democratising access to data

Public cloud deployments have become irresistible in terms of flexibility, low barriers to entry, security, and developer friendliness. But the sheer inertia of traditional data lakes make them difficult to transition to cloud. In this talk we'll look at examples of how leading companies have made the transition using open source technologies and hybrid strategies.

Instead of following a "lift and shift" strategy for moving data lake workloads to the cloud, there are new considerations unique to cloud that should be considered alongside traditional approaches related to compute (eg, GPU, FPGA), storage (object store vs. file store), integrations, and security.

Viewers will take away techniques they can immediately apply to their own projects.

Many organisations aspire to become digital, data driven enterprises. In these organisations data is viewed as a critical asset, both to generate new digitally based products and services, and to guide and improve business operations and decision making. But many companies are failing to live up to this aspiration. They struggle to develop and implement data strategies that align with, and help to deliver, new business strategies.

This webinar will explore what becoming ‘data driven’ really means, examines some of the reasons why many organisations are failing to realise their ambitions, and propose ways of overcoming the challenges. Key to these is a strong emphasis on the increasingly critical importance of established data management disciplines, especially Data Governance, Data Quality and MDM, which all have a critical role to play in the digital business of the future.

This session will explore:

•What is a data driven organisation and how does it differ from a traditional company?
•The main challenges of creating a data driven organisation
•Building a data driven capability - the role of business and IT
•The central importance of a business aligned Data Strategy and how to achieve it
•Why a successful data strategy needs an integrated focus on Data Governance, Data Quality and MDM

The concept of Data lakes evolved to address challenges and opportunities in managing big data.

Organizations are investing massive amounts of time and money to upgrade existing data infrastructures and build data lakes whether on-premises or in the cloud.

This talk will discuss architectures and design options to implement data lakes with open source tools. Also covered are challenges of upgrade & migration from existing data warehouses, metadata management, supporting self-service and managing production deployments.

As an Enterprise customer, you are potentially using IBM Z in a hybrid cloud implementation. Let's understand how to benefit from cloud access to mainframe data without moving it outside z; thereby improving security, reducing integration challenges and answering your GDPR auditor's needs.